The Accuracy of Detection of Artificial Intelligence Second Mesio-buccal Canal of Maxillary First Molars on CBCT Images

The Accuracy of Computer Aided Detection of Second Mesio-buccal Canal of Maxillary First Molars on CBCT Images Using Deep Learning Model (Artificial Intelligence): Diagnostic Accuracy Study

CAD systems are computer applications that assist in the detection and/or diagnosis of diseases by providing an unbiased "second opinion" to the image interpreter, aiming at improving accuracy and reducing time for analysis. With the rapid growth of Deep Learning (DL) algorithms in image-based applications, CAD systems can now be trained by DL to provide more advanced capability (ie, the capability of artificial intelligence [AI]) to best assist clinicians.

Study Overview

• Status: Recruiting
• Conditions: Artificial Intelligence
• Intervention / Treatment: Diagnostic test: deep learning model

Detailed Description

Countless studies and discussions have been based on the existence of a second canal in the mesiobuccal (MB) root of the maxillary molars , since it is strongly believed that one of the foremost reasons for endodontic failure in maxillary first molars is the difficulty of detecting and treating those second mesiobuccal (MB2) canals .The literature reveals that although MB2 canals of maxillary first molars have been found in more than 70% of in vitro studies , they were detected clinically in less than 40% of cases . Cone beam computed tomography (CBCT) is an imaging modality in the field of endodontics that has several advantages, including the ability to perform three-dimensional (3D) imaging of root canal systems with lower radiation doses, higher resolution, and no superimposition . Researchers have evaluated the efficiency of CBCT when it comes to identifying MB2 canals, and CBCT has been suggested to be a reliable method for the detection of these canals. However, in clinically relevant situations, such a smaller lesions on root-filled teeth, CBCT accuracy is greatly reduced (sensitivity 0.63, specificity 0.69) . Moreover, clinician dependent interpretation of CBCT imaging still suffers from low inter- and intra-observer agreement.

Computer-aided detection and diagnosis (CAD) has been widely applied to biomedical image analysis outside of dentistry .

• Study Type: Observational
• Enrollment (Anticipated): 50

Contacts and Locations

• Study Contact: Name: Sally Mansour, Masters; Phone Number: +201019932383; Email: sally.mansour@dentistry.cu.edu.eg
• Study Contact Backup: Name: Ahmed MFM Magdy, MCS; Phone Number: +201019932383; Email: ahmed_magdy@dentistry.cu.edu.eg

Study Locations

• Egypt
  • Cairo, Egypt, 12611
    • Recruiting
    • Faculty of Dentistry Cairo University
    • Contact: Faculty ODC university; Phone Number: 01066365552; Email: sally.mansour@dentistry.cu.edu.eg

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

CBCT scans showing maxillary first molars with resolution not more than 0.1 mm voxel size.The CBCT data of this study will be obtained from the CBCT data base available at the department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Cairo University, Cairo, Egypt and from multiple private radiology centres using the same machine brand with the same parameters. CBCT scans of Egyptian patients who have already been subjected to CBCT examination as part of their dental diagnosis and/or treatment planning from January 2020 to December 2022 will be included according to the proposed eligibility criteria.

Description

Inclusion Criteria:

• • CBCT scans showing erupted maxillary 1st molar.

  • Vovel size not exceeding 0.1mm.
  • Maxillary molars showing complete root formation.
  • Carious or Non-carious tooth.

Exclusion Criteria:

• • Maxillary first molars with developmental anomalies, external or internal root resorption, root canal calcification, previous root canal treatment, post restorations, and/or root caries.

  • CBCT images of sub-optimal quality or artifacts / high scatter interfering with proper assessment.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
CBCT Images of Maxillary 1st molars	Diagnostic test: deep learning model; deep learning model developed by computer science expert and based on convolution neural network , and trained by our datasets.; Other Names: artificial intelligence tool

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
accuracy of detection of MB2	detection of MB2 on CBCT images of maxillary first molars using deep learning model	baseline

Collaborators and Investigators

• Sponsor: Cairo University
• Investigators: Study Director: Enas Anter, Ph.D, Cairo university

Publications and helpful links

General Publications

• Blattner TC, George N, Lee CC, Kumar V, Yelton CD. Efficacy of cone-beam computed tomography as a modality to accurately identify the presence of second mesiobuccal canals in maxillary first and second molars: a pilot study. J Endod. 2010 May;36(5):867-70. doi: 10.1016/j.joen.2009.12.023. Epub 2010 Feb 21.
• Kulild JC, Peters DD. Incidence and configuration of canal systems in the mesiobuccal root of maxillary first and second molars. J Endod. 1990 Jul;16(7):311-7. doi: 10.1016/s0099-2399(06)81940-0.
• Alacam T, Tinaz AC, Genc O, Kayaoglu G. Second mesiobuccal canal detection in maxillary first molars using microscopy and ultrasonics. Aust Endod J. 2008 Dec;34(3):106-9. doi: 10.1111/j.1747-4477.2007.00090.x.
• Gorduysus MO, Gorduysus M, Friedman S. Operating microscope improves negotiation of second mesiobuccal canals in maxillary molars. J Endod. 2001 Nov;27(11):683-6. doi: 10.1097/00004770-200111000-00008.
• Weine FS, Hayami S, Hata G, Toda T. Canal configuration of the mesiobuccal root of the maxillary first molar of a Japanese sub-population. Int Endod J. 1999 Mar;32(2):79-87. doi: 10.1046/j.1365-2591.1999.00186.x.
• Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. Deep Learning for the Radiographic Detection of Apical Lesions. J Endod. 2019 Jul;45(7):917-922.e5. doi: 10.1016/j.joen.2019.03.016. Epub 2019 Jun 1.
• Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, Fujita H, Ariji E. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol. 2019 Mar;48(3):20180218. doi: 10.1259/dmfr.20180218. Epub 2018 Nov 9.
• Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Ozyurek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020 May;53(5):680-689. doi: 10.1111/iej.13265. Epub 2020 Feb 3.

Study record dates

Study Major Dates

• Study Start (Anticipated): May 1, 2022
• Primary Completion (Anticipated): September 1, 2023
• Study Completion (Anticipated): October 1, 2023

Study Registration Dates

• First Submitted: April 15, 2022
• First Submitted That Met QC Criteria: April 20, 2022
• First Posted (Actual): April 21, 2022

Study Record Updates

• Last Update Posted (Actual): April 21, 2022
• Last Update Submitted That Met QC Criteria: April 20, 2022
• Last Verified: April 1, 2022

More Information

Terms related to this study

• Other Study ID Numbers: CBCT AI 7-1-1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No